A major statistical problem in mental health research has been how to translate subject's scores on a set of dimensions into a meaningful and reliable classification of individuals. This problem is particularly significant since decisions (e.g., assignment to different treatments) should be made on the basis of a subject's entire pattern of scores, rather than scores on a single dimension. The purpose of this research was to develop some new clustering algorithms that include corrections for unreliability, and to compare these algorithms using computer generated data sets. Parametric comparisons between hierarchical clustering algorithms were made, using several evaluative criteria including accuracy, precision, stress, and the cophenetic correlation coefficient. Algorithms using the product-moment correlation as the measure of similarity were superior to those using Euclidean distance, and those algorithms employing empirical Bayesian corrections for unreliability performed better than conventional algorithms.